Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient‐centric, and simultaneously more precise and individualized care to ensue. Digital twins combined with advances in Artificial Intelligence (AI) have the potential to facilitate the integration and processing of vast amounts of heterogeneous data stemming from diversified sources. Hence, in healthcare this can provide enhanced diagnosis and treatment decision support. In applying digital twins in combination with AI to complex healthcare contexts to assist clinical decision making, it is also likely that a key current challenge in healthcare; namely, providing better quality care which is of high value and can lead to better clinical outcomes and a higher level of patient satisfaction, can ensue. In this focus article, we address this proposition by focusing on the case study of cancer care and present our conceptualization of a digital twin model combined with AI to address key, current limitations in endometrial cancer treatment. We highlight the role of AI techniques in developing digital twins for cancer care and simultaneously identify key barriers and facilitators of this process from both a healthcare and technology perspective.This article is categorized under: Application Areas > Health Care

Understanding the corrosion risk of a pipeline is vital for maintaining health, safety and the environment. This study implemented a data-driven machine learning approach that relied on Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), Feed-Forward Artificial Neural Network (FFANN), Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) to estimate the corrosion defect depth growth of aged pipelines. By modifying the hyperparameters of the FFANN algorithm with PSO and using PCA to transform the operating variables of the pipelines, different Machine Learning (ML) models were developed and tested for the X52 grade of pipeline. A comparative analysis of the computational accuracy of the corrosion defect growth was estimated for the PCA transformed and non-transformed parametric values of the training data to know the influence of the PCA transformation on the accuracy of the models. The result of the analysis showed that the ML modelling with PCA transformed data has an accuracy that is 3.52 to 5.32 times better than those carried out without PCA transformation. Again, the PCA transformed GBM model was found to have the best modeling accuracy amongst the tested algorithms; hence, it was used for computing the future corrosion defect depth growth of the pipelines. This helped to compute the corrosion risks using the failure probabilities at different lifecycle phases of the asset. The excerpts from the results of this study indicate that my technique is vital for the prognostic health monitoring of pipelines because it will provide information for maintenance and inspection planning.

Nanostructures are playing significant roles in the development of new functions and the enhancement of the existing functions of industrial devices such as sensors, transistors, diodes, lithium-ion batteries, and photovoltaic cells, due to their piezoelectricity, biocompatibility, and pyroelectrical characteristics. This research focused on the review of the characteristics of different nanostructures and nanomaterials such as ZnO, ZnS, MoS₂, GO, TiO₂, SnO₂, and Fe₂O₃, their fabrication techniques, growth mechanisms, and applications. The factors affecting the growth mechanisms, the crystallographic natures, growth models of anisotropic nanostructures, and growth of nanocrystals, were also highlighted in this research. The existence of lattice mismatch, differential thermal expansion, and high deposition temperature, have affected uniform deposition of nanoparticles on substrates and caused heteroepitaxy, which has resulted in defective nanostructures. Although heteroepitaxy has negatively affected the characteristics and potential applications of nanostructures, it has also opened new research frontiers for potential new applications of nanostructures. The use of nanostructures for gas sensing is attributed to the high specific area, change of resistance on exposure to gases, and high photoconduction abilities, while the photon-carrier collection abilities and anti-reflectance qualities are vital for solar photovoltaic cells. Nanostructures have also been used as coating pigments to prevent corrosion of facilities, reduce urban heat islands and energy consumptions, due to the near infrared (NIR) reflective characteristics.